describe_metricFree. Returns full details for one metric id from list_metrics: parameters, request/response schemas, price, and how to fetch it.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Metric id from list_metrics, e.g. "metrics/vpin" or "options/gex". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds value by noting the operation is free and detailing the return content. It does not contradict annotations and provides additional behavioral context (no cost, detailed output).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the key benefit ('Free') and immediately conveys the tool's purpose and output. There is no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with one parameter, the description covers what the tool returns and its source. It lacks explicit details about error behavior or output format, but given the lack of an output schema, it provides sufficient context for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% description coverage for the single parameter `id`, including an example. The tool description only restates that the id comes from `list_metrics`, adding no new semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that it returns full details for a single metric id, specifying the output includes parameters, request/response schemas, price, and fetch instructions. It distinguishes itself from sibling tools like `list_metrics` and `get_metric` by focusing on detailed retrieval of one metric.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly ties usage to `list_metrics` by referencing 'metric id from list_metrics', but does not explicitly state when to use this tool versus alternatives or when not to use it. There is no mention of prerequisites or context beyond having the id.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: computing VaR, computing greeks, describing metrics, fetching metrics, checking payment requirements, and listing metrics. No overlap in functionality.
Tool names follow a consistent verb_noun pattern: compute_, describe_, get_, list_. All use snake_case and imperative verbs, making the set predictable.
With 6 tools, the server is well-scoped. It covers listing, describing, fetching, and computing metrics, plus a payment helper, without being bloated or too sparse.
The tool set provides end-to-end coverage for the DripMetrics API: discover metrics (list_metrics), get details (describe_metric), fetch data (get_metric), compute derived values (compute_portfolio_var, compute_spread_greeks), and handle payments (get_payment_requirements). No obvious gaps.